CSIRO PUBLISHING
www.publish.csiro.au/journals/ijwf
International Journal of Wildland Fire, 2007, 16, 361–377
Bushfires ‘down under’: patterns and implications
of contemporary Australian landscape burning
Jeremy Russell-SmithA,B,J , Cameron P. YatesC , Peter J. WhiteheadA,C ,
Richard SmithD , Ron CraigD , Grant E. AllanB,E , Richard ThackwayF ,
Ian FrakesF , Shane CridlandG , Mick C. P. MeyerH and A. Malcolm GillI
ATropical
Savannas Management Cooperative Research Centre, Darwin, NT, Australia.
NT, Darwin, NT, Australia.
C Department Natural Resources, Environment & the Arts, Palmerston, NT, Australia.
D Department Land Information, Perth, WA, Australia.
E Desert Knowledge Cooperative Research Centre, Alice Springs, NT, Australia.
F Bureau of Rural Sciences, Canberra, ACT, Australia.
G Department Environment & Heritage, Canberra, ACT, Australia.
H CSIRO Marine & Atmospheric Research, Aspendale, Vic, Australia.
I CSIRO Plant Industry, Canberra, ACT, Australia.
J Corresponding author. Email: [email protected]
B Bushfires
Abstract. Australia is among the most fire-prone of continents. While national fire management policy is focused
on irregular and comparatively smaller fires in densely settled southern Australia, this comprehensive assessment of
continental-scale fire patterning (1997–2005) derived from ∼1 km2 AdvancedVery High Resolution Radiometer (AVHRR)
imagery shows that fire activity occurs predominantly in the savanna landscapes of monsoonal northern Australia. Statistical models that relate the distribution of large fires to a variety of biophysical variables show that, at the continental
scale, rainfall seasonality substantially explains fire patterning. Modelling results, together with data concerning seasonal
lightning incidence, implicate the importance of anthropogenic ignition sources, especially in the northern wet–dry tropics
and arid Australia, for a substantial component of recurrent fire extent. Contemporary patterns differ markedly from those
under Aboriginal occupancy, are causing significant impacts on biodiversity, and, under current patterns of human population distribution, land use, national policy and climate change scenarios, are likely to prevail, if not intensify, for decades to
come. Implications of greenhouse gas emissions from savanna burning, especially seasonal emissions of CO2 , are poorly
understood and contribute to important underestimation of the significance of savanna emissions both in Australian and
probably in international greenhouse gas inventories. A significant challenge for Australia is to address annual fire extent
in fire-prone Australian savannas.
Additional keywords: AVHRR, biomass burning, fire mapping, greenhouse gas emissions, remote sensing, satellite
imagery, savanna burning.
Introduction
Australia is recognised for a biota forged by and adapted to
recurrent patterns of fire (Gill et al. 1981; Bradstock et al.
2002). Global remote sensing studies demonstrate that it is
one of the most flammable of continents (Dwyer et al. 2000;
Duncan et al. 2003; Tansey et al. 2004; Carmona-Moreno et al.
2005). In concert with global patterning of landscape-scale fire,
Australian biomass burning and associated emissions of important atmospheric greenhouse trace gases originate mostly from
tropical savanna biomes (Craig et al. 2002; AGO 2006). However, Australian national fire management and policy debate
is strongly determined by well publicised, tele-visual bushfire
conflagrations in relatively densely settled southern Australia.
Such fires dominate policy responses given that they periodically
(e.g. in the case of the south-east Australian bushfires of 2002–
2003) cause major social disruption, including extensive loss of
© IAWF 2007
property and human life. Popular media treatment of Australian
(and much international) landscape fire as at best unwelcome,
and at worst catastrophic, ignores the reality that over much of
the continent fire is actively employed as an important tool to
promote both production and conservation goals (Pyne 1991,
2006; Dyer et al. 2002; Cary et al. 2003).
Continental-wide understanding of fire occurrence in Australia has developed rapidly over the past decade with the
application of daily observations of the relatively coarse resolution Advanced Very High Resolution Radiometer (AVHRR)
instrument (pixel size ∼1.1 × 1.1 km2 at orbital nadir) on the
United States’ National Oceanic and Atmospheric Administration (NOAA) series of satellites. Assembled fire observation
data are available from 1997 for the whole of the continent,
and from 1990 for Western Australia and the Northern Territory (Craig et al. 2002; Meyer 2004). Components of these data
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Int. J. Wildland Fire
have been applied to national biodiversity (Russell-Smith et al.
2002) and biomass burning and greenhouse emissions (Meyer
2004; AGO 2006) audits, regional ecological assessments (Allan
and Southgate 2002; Williams et al. 2002; Russell-Smith et al.
2003b; Spessa et al. 2005), and web-based fire monitoring and
management applications (e.g. http://www.firewatch.dli.wa.gov.
au; http://www.firenorth.org.au; http://sentinel2.ga.gov.au/acres/
sentinel/, accessed 29 July 2007).
As a contribution to the emerging understanding of global
trends and patterns of biomass burning, we describe the seasonal
distributions of active fires and large burned areas (respectively, fire hot spots, FHS, between 1999 and 2005, and fire
affected areas, FAA, between 1997 and 2004) derived from
AVHRR imagery. For validated FAA (generally >4 km2 : Craig
et al. 2002; Yates and Russell-Smith 2002), we assess seasonal patterning with reference to continental-scale rainfall
patterns, vegetation productivity (Normalised Difference Vegetation Index – NDVI), vegetation type, fuel type, lightning
incidence, topography (elevation, surface roughness), cadastral
density and land use surfaces. Hypotheses (refer to Method section) concerning putative relationships of the above biophysical
parameters to spatial and temporal patterning in fire incidence
and extent are explored using statistical models.
Our goal is to provide the first national-scale, rigorously
quantitative assessment of the spatial extent of fire in the
entire Australian landscape and explore important environmental influences on these broad-scale fire patterns. Our treatment
provides context for consideration of management, biodiversity
and greenhouse gas emission implications of fire management
practice and performance in Australia.
Materials and methods
Australian active fire detection and mapping datasets
Two Australia-wide fire mapping datasets were derived from
NOAA-AVHRR imagery: active fires referred to as FHS, for
the period 1999–2005; and nine-day mapping of large burnt
areas referred to as FAA, for the period 1997–2004. Respective datasets provide different perspectives of the seasonality
and distribution of fires, and it is useful to distinguish between
these at the outset.
Following radiometric and geometric correction of captured AHVRR data with Common AVHRR Processing Software
(CAPS) (CSIRO 2006), FHS were detected daily across the
entire continent from daily NOAA satellite evening passes. Reliable FHS detection from daytime overpasses is not possible in
highly reflective Australian desert regions, which causes the
reflected solar irradiance to either saturate the AVHRR midthermal infrared (3.84 mm) sensor or cause significant errors of
commission. An automated detection algorithm, modified from
Lee and Tag (1990), was used (Craig et al. 2002). Detection sensitivity is such that sub-pixel fires (<1 km2 ) are easily detected
(Dozier 1981); e.g. it is common for intense heat sources from
offshore oil platforms and steel or nickel refineries to be detected.
Significant errors of omission are associated particularly with
cloudiness (including in peak burning seasons in respective Australian regions), frequency of satellite overpass and night-time
sampling bias (Craig et al. 2002; Gill and van Didden 2002;
Smith et al. 2006). While FHS data are not used for modelling
J. Russell-Smith et al.
Jan–Mar
Apr–Jun
Jul–Sep
Oct–Dec
Fig. 1. Seasonal (quarterly) distribution of FHS, 2002. Regions depicted
are those as described in Fig. 3.
Legend
Unburnt
Burnt once
Burnt twice
Burnt 3 times
Burnt 4 times
Burnt 5 times
Burnt 6 times
Burnt 7 times
Burnt 8 times
Fig. 2. Frequency of large fires derived from FAA mapping, 1997–2004.
Circled area denotes 2002–2003 southern Australian bushfires. Regions
depicted are those as described in Fig. 3.
purposes (see below), an example of the quarterly distribution of
recorded FHS for one year (2002) is presented in Fig. 1. Given
these significant detection issues, we note that while available
FHS data sample but a small proportion of fire activity, they
nevertheless provide a large, multi-year statistical sample for
describing fire seasonality.
For FAA, radiometric- and geometric-corrected data were
mapped over the eight year period, 1997–2004, with a semiautomated change detection procedure (Craig et al. 2002) from
NOAA-AVHRR daytime imagery using visible, near infrared
and thermal bands (Bands 1, 2, 5), every nine days of the repeat
NOAA cycle. Assembled FAA data provide the basis of the fire
map data referred to throughout this paper. The frequency of
large fires over the period 1997–2004 is given in Fig. 2.
Errors associated with FAA mapping include sustained
cloudiness and omission of many small fires, especially those
less than 2–4 km2 (Craig et al. 2002; Yates and Russell-Smith
Bushfires ‘down under’
Int. J. Wildland Fire
363
Table 1. Summary of data types and derived variables and surfaces used in statistical analyses (refer to text for details)
Data type
Description
Derived variables/surfaces
Grid tile
Continental grid of 0.5◦ × 0.5◦ cells (n = 3026), used as basis
for segmenting all data surfaces
Continental 0.5◦ × 0.5◦ grid tile
FAA mapping
1997–2004
Derived from semi-automated mapping from NOAA-AVHRR
imagery at 1.1 × 1.1 km2 pixel size
Proportion burnt each quarter, annually
Occurrence of fire in each quarter, annually
Frequency of fire over eight year period
Rainfall
Derived from continental interpolated rainfall surface
of 0.25◦ × 0.25◦ cells, 1969–2004
Continental rainfall classification (RAINCLASS),
derived from unsupervised classification of all quarterly
rainfall data 1969–2004 (RAINCLASS)
Mean rainfall per quarter, annually, 1969–2004
Percent deviation from 1969–2004 quarter, annual means,
for periods: (a) 1997–2004; (b) 1993–1996. Latter period
used for calculating up to 4 years antecedent rainfall
Antecedent rainfall (1–4 years) for period 1997–2004,
calculated: (a) annually; (b) by rainyear (July to June)
NDVI
Derived from continental 14-day 0.01◦ × 0.01◦
cloud-masked NDVI surfaces, 1992–2004
Mean maximum NDVI per quarter, 1992–2004
Percent deviation from 1992–2004 quarter means for periods:
(a) 1997–2004; (b) 1993–1996, as per rainfall data
Antecedent maximum NDVI (1–4 years) for period 1997–2004,
calculated: (a) annually; (b) by rainyear (July to June)
Vegetation type
23 types derived from continental vegetation mapping, including
one category representing cleared/modified native vegetation
Dominant vegetation type (domveg)
Percentage of cell with dominant vegetation type (%domveg)
Number of vegetation types per tile
Fuel type
15 types derived from continental vegetation mapping
surfaces, as a grid of 0.5◦ × 0.5◦ cells representing the
dominant fuel type per cell
Dominant fuel type (domfuel)
Percentage of cell with dominant fuel type (%domfuel)
Number of fuel types per tile
Elevation
Derived from continental 9 Digital Elevation Model
Maximum elevation
Elevation, coefficient of variation
Surface roughness
Derived from continental 9 Digital Elevation Model
Range in elevation (elevrange)
Range in elevation, coefficient of variation (cvrough)
Land use
Six types (including water) derived from continental mapping
Dominant land use (domluf, domlub)
Percentage of cell with dominant land use (%domlu)
Number of land use types per cell (numlus)
Cadastral density
Derived from national database, but excluding parcels <40 ha
Average size of land parcels ≥40 ha (avparea40)
Number of land parcels ≥40 ha per cell (numparc40)
2002). While fires under humid cloudy conditions are likely to
be generally restricted (e.g. monsoonal ‘wet season’ conditions
in northern Australia), notable exceptions occur; e.g. extensive
fires ignited by lightning strike in inland regions associated with
storm activity before the onset of monsoonal activity (Allan and
Southgate 2002).
Assessment of the accuracy of FAA mapping derived from
AVHRR imagery has been undertaken for northern Australian
savanna systems by adopting a regression methodology similar
to that outlined by Eva and Lambin (1998). Overall agreement between fire mapping derived from LANDSAT TM with
ground-truth data ranged between 84 and 88% for respective
study scenes. Regression analyses indicated that the degree of
correspondence (r2 ) between LANDSAT and AVHRR fire mapping was generally high, ranging from a respectable 0.81 to
a modest 0.41 (Yates and Russell-Smith 2002; Russell-Smith
et al. 2003a). Regression slopes for all but one scene indicated
that fire-mapping fromAVHRR consistently under-estimated the
‘true’ extent of burning by as much as 10–20%.
Yates and Russell-Smith (2002) also provide a detailed comparison of annual fire size distributions derived from AVHRR
v. LANDSAT imagery for three north Australian LANDSAT
scenes. While more than 90% of individual fires were found to
be omitted from AVHRR FAA mapping given their small size,
such fires constituted less than 3% of the total area burnt. Similar
observations concerning the influence of small numbers of fires
contributing a large proportion of the FAA are widely reported
(Kasischke and French 1995; Keeley et al. 1999; Stocks et al.
2003). In summary, the broad patterning of fire as described here
for the fire-prone northern and central Australia is likely to be
generally representative, at least with respect to the scale of coverages and analyses presented. The data presumably understate
the extent of burning in more inland areas prone to lightning
activity, and more generally associated with mapping error.
Data surfaces
The following data surfaces were derived for summary statistics
and modelling analyses (Table 1).
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Int. J. Wildland Fire
J. Russell-Smith et al.
8
7
6
7
10
8
9
6
4
9
2
5
1
Rainfall class
1: Southern arid
2: Central arid
3
3: Southern mesic
4: Northern semi-humid
5: East coast semi-humid
6: Northern sub-coastal humid
7: Northern coastal humid
8: Top End and Cape York humid
9: West Tropics mesic
10: Northern Cape York humid
Fig. 3.
3
3
RAINCLASS classification of 10 regions (refer to text for details).
Half-degree tile
All data surfaces assembled for analyses (below) were compiled with respect to a common tile set of 0.5◦ × 0.5◦ cells,
aligned to whole degrees, covering the Australian land mass.
There were 3063 tiles that contained at least some land area.
However, only 3025 tiles contained enough land to generate useful information for all data surfaces and most analyses are based
on these comprehensively described tiles.
FAA
The area, proportion and occurrence (presence/absence) of
burning, derived from AVHRR FAA mapping (source: Department of Land Information, WesternAustralia), was calculated for
each 0.5◦ × 0.5◦ tile, for each quarter year (January to March,
April to June, July to September and October to December),
over the period 1997–2004. To make these calculations, a grid
of 10 000 cells was intersected with the net of 0.5◦ × 0.5◦ tiles,
and the cells were attributed as burned or unburned and the
proportion of cells in each tile calculated. The proportion of
cells recorded as having been burned in each sample period was
treated as the proportion of tile area burned. In addition, the quarterly, annual or rain–year frequency of burning (ranging from
0 to 8 times) was calculated for each tile over the eight year
period. These summaries were also used to identify the tiles that
showed no evidence of fire during the eight year sample period v.
those that were burned at all at any time during the study period.
Lightning
Lightning data for 2004–2005 were received in real-time from
the World Wide Lightning Location Network (WWLLN) (Lay
et al. 2004). The WWLLN involves twenty lightning location
sensors in the very low frequency (VLF) band (3–30 kHz).
Observations in the VLF band are dominated by impulsive signals from lightning discharges called ‘sferics’. Determination of
each lightning stroke location requires the time of group arrival
(TOGA) from at least four WWLLN sensors (Dowden et al.
2002). As the ‘sferics’ are dispersed through the ionosphere,
the sensors may be several thousands of kilometres distant from
the stroke. WWLLN records both cloud-to-ground and in-cloud
lightning as long as it has a large peak current. Accuracy of the
lightning location is generally 1–2 km (Dowden et al. 2002).
For the purposes of this paper, the number of lightning strikes
were aggregated into monthly intervals per rainfall zone (or
RAINCLASS, see below).
Rainfall
Data were acquired from the Bureau of Meteorology (Commonwealth of Australia – CoA) as monthly rainfall grids for
the period from 1969 to 2004. The grids, each of 0.25◦ × 0.25◦
dimension, were computer generated using the Barnes successive correction technique (Jones and Weymouth 1997) from
irregularly spaced point-based rainfall observations; ∼6000
rainfall stations across Australia contribute to this database.
Assembled rainfall data were used subsequently as follows.
First, a twenty-class rainfall regionalisation (RAINCLASS)
was derived using quarterly data in an unsupervised classification. From this, a final 10 class classification was derived
by combining eleven geographically contiguous, small, high
rainfall classes in north-east Queensland into one class, which
represents the relatively small wet tropics region (Fig. 3). Second,
for the purposes of modelling the effects of antecedent rainfall
on FAA, mean rainfall was calculated for each 0.5◦ × 0.5◦ tile
separately for each quarter, calendar year, and northern rainyear
Bushfires ‘down under’
(July to June) from the 36 years of rainfall observations. For each
cell, annual deviations from the long-term (36 year) mean were
then calculated and expressed as a proportion of the mean. This
approach allowed for the use of standardised rainfall data per
cell for inter- and intra-regionalised analyses, rather than highly
variable (between-cell) absolute measures.
Normalised Difference Vegetation Index (NDVI)
Fourteen day 0.01◦ × 0.01◦ cloud-masked NDVI surfaces
derived from NOAA-AVHRR sensors between 1992 and 2004
(source: Department of the Environment & Heritage, CoA), were
maximum-value composited to 28-day surfaces. These were then
aggregated into 0.5◦ × 0.5◦ tiles as the average value of the
non-cloud, non-water pixels for each cell. The value for each
0.5◦ × 0.5◦ tile for each time-slice was extracted to give a time
sequence for each tile, and then splined to give daily values.
Splined values were cut into months and then averaged by month,
then quarter. Quarterly mean values and standard deviations were
calculated for each of the four quarters across the 13 years. For
each tile, the difference of each quarter from the corresponding
13 year mean was calculated and expressed as a proportion of
the mean or standard deviation for that quarter, scaled so that
both indices took the value 100 at the mean. Surfaces were then
produced for these respective values. Variations in these indices
are interpreted to reflect within-tile temporal differences in the
amount and/or condition of vegetation biomass. High values
may reflect increased availability of fuel in subsequent fire seasons. Analyses showed very little difference in statistical models
using the two indices, and only analyses based on the standard
deviation index are reported here.
Vegetation and fuel types
Twenty three vegetation types (including one that represents
modified/cleared vegetation) were derived from the national
Major Vegetation Group (MVG) (NLWRA 2001) mapping.
Fifteen national Major Bushfire Fuel Groups (MBFGs: Fig. 4a)
were derived from MVG and, for modified vegetation, Integrated
Vegetation Cover (IVC) (Thackway et al. 2004) datasets, following Gill et al. (2006). Both MVG and IVC mapping is compiled
from regional mapping at a range of scales, from 1:5 000 000 to
1:25 000. Each 0.5◦ × 0.5◦ cell was attributed to the dominant
MBFG type. Additional attributes were derived as the number
of MBFGs in each quarter tile and the proportion of each tile
covered by the dominant MBFG. The proportion of MBFGs
occupying each RAINCLASS is given in Table 3.
Elevation and surface roughness
Elevation and surface roughness were derived from a 9
Digital Elevation Model (DEM) (source: Australian Centre for
Remote Sensing, CoA). Surface roughness was derived from
the DEM by applying a focal range, and calculated as the difference between maximum and minimum elevation, within a 7 × 7
moving window pixel area (Fig. 4b). Mean values of surface
roughness and elevation were then calculated for each 0.5◦ cell.
Land use
Six primary levels of land use were distinguished in order of
generally increasing levels of intervention or potential impact
on the natural landscape, following Stewart et al. (2001):
Int. J. Wildland Fire
365
(1) conservation, natural environments; (2) production from
relatively natural environments; (3) production from dryland
agriculture and plantations; (4) production from irrigated agriculture and plantations; (5) intensive uses; (6) water (Fig. 4c).
Each 0.5◦ cell was attributed with a dominant land use type, proportion of respective land uses, and the number of different land
uses (from a finer resolution dataset comprising 23 classes).
Cadastral/property density
To explore relationships between infrastructure/property density (and secondarily, as a surrogate for population density), densities of properties per 0.5◦ cell were calculated from the national
2001 CadLite dataset (http://www.psma.com.au/datasets/cadlite,
accessed 1 January 2007), excluding all properties less than
40 ha in size (Fig. 4d). This size was selected arbitrarily to eliminate most urban developments. Properties with boundaries that
extended across more than one quarter tile were excluded, so
that estimates of mean property area and number of properties in individual tiles are based only on those properties that
fell entirely within each tile. The effect of this procedure is to
slightly reduce estimates of property size in very remote areas,
especially northern Australia.
Statistical analyses
Attributes of individual quarter tiles as summarised above were
stored and manipulated using SAS Institute (2002). To explore
associations of these patterns of fire extent and incidence with
landscape variables, generalised linear modelling was done
using R Development Core Team (2005). Model selection procedures used the Akaike Information Criterion (AIC) applied as
described by Burnham and Anderson (2002). In brief, models
are ranked according to their fit to the data as summarised in the
AIC value. The model with the lowest AIC value is treated as
displaying the best fit to the data and compared with competing
models in the set. Competing models are treated as having less
support the further their AIC departs from the best model. As a
rule of thumb, models with AIC exceeding that of the best model
by more than 10 are treated as having effectively no support.
Additional details are given in tables reporting statistical models.
In all cases, proportions (not including relative frequencies)
were arcsine transformed before statistical analysis.
FAA events in 0.5◦ tiles are summarised over eight years to
permit separate examination of three response variables:
• Proportion of 0.5◦ tile burned annually, assuming Gaussian
error distribution and employing an identity link
• Frequency with which any portion of 0.5◦ tile had fire during
1997–2004, assuming binomial distributions and using a logit
link
• Tiles with some fire at least once during eight years v.
tiles without fire assuming binomial error distribution and
employing a logit link (see Crawley [2002] for details).
(1) Spatial variation in fire incidence and extent
In the first set of analyses, a candidate set of models was identified for relating these whole-of-study summary relationships of
fire incidence to landscape variables, namely dominant vegetation types (the vegetation type covering the largest area within
the tile) and their representation (percentage of area of each tile
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Int. J. Wildland Fire
covered by dominant vegetation), dominant fuel types, dominant
land uses, cadastral density, elevation, range of elevation, topographic roughness and the rainfall class to which the tile was
assigned. Statistical models were derived from the set of 3025
tiles for which data were available from all relevant surfaces.
Our selection of these ‘explanatory’ variables embedded several
hypotheses about those features of the landscape expected to
influence broad scale patterns of fire, in terms of both frequency
and areal extent. In brief, they are:
Climatic zonation: Climate, particularly rainfall quantum and
temporal patterns (both seasonal and inter-annual) influence
J. Russell-Smith et al.
rates of plant growth (Nix 1982), temporal patterns of growth
and senescence of plants, and physical conditions favouring
or suppressing fire. It was predicted that there would be substantial variation among climatic zones based on rainfall, with
fire incidence and extent (either or both) being greater in
(1) zones that show intense within-year seasonality of rainfall and so regular alternating periods of growth and curing,
and (2) in (mostly arid) areas of unpredictable rainfall where
periods of one or more seasons favouring growth alternate
with often longer periods of drought.
Fuel type: The structure and floristic composition of vegetation
cover influences the quantity of fuel, its flammability and
(a)
Legend
Unknown
Acacia shrublands
Mallee woodlands and shrublands
Chenopod shrubs, samphire shrubs and forblands
Hummock grasslands
Tussock grasslands
Open woodlands
Low closed forests and closed shrublands
Rainforest and vine thickets
Eucalypt open forests
Eucalypt tall open forests
Eucalypt low open forests
Agricultural
Urban
Water Features
(b)
Slope (m)
High: 51
Low: 0
Fig. 4. Examples of derived spatial surfaces used in statistical analyses, (a) Major Bushfire Fuel Groups, (b) surface roughness,
(c) major land use type, (d) cadastral/property density. Refer text for details.
Bushfires ‘down under’
Int. J. Wildland Fire
367
(c)
Legend
Conservation Natural Environments
Production from Relatively Natural Environments
Production from Dryland Agriculture and Plantations
Production from Irrigated Agriculture and Plantations
Intensive uses
Water
(d )
0–1
1–10
10–100
100–1000
1000–10 000
Fig. 4.
management practices, including deliberate use of fire. The
fuel variable derives from vegetation type, but recognises a
smaller number of classes placing greater emphasis on vegetation structure. It was expected that both fire incidence and
extent would vary among fuel types within rainfall classes,
being greater in vegetation types with greater proportions
of grass and other more flammable vegetation types (e.g.
eucalypt-dominated).
Topography: It is expected that fire incidence (because of limited
access) and especially extent (because of interruption of fire
movement by natural barriers) will be lower in areas of acute
variation in elevation.
(Continued)
Land use: The dominant land use is expected to be associated
with variation in fire incidence and extent through its influence on fuel availability and differences in active use or
suppression of fire. Fire incidence and extent is expected to
be higher in less intensively modified landscapes, and where
the dominant land use (e.g. pastoralism) often includes use
of fire in management.
Cadastral density (parcel size): Use of fire as a management tool
is expected to be greater in landscapes where property sizes
are large. The capacity to exclude or control unwanted fire is
also lower in such places, leading to the prediction that fire
incidence and extent will vary directly with property size.
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Int. J. Wildland Fire
J. Russell-Smith et al.
1200
The best models built from these explanatory variables are
identified for each of the three response variables using the
methods described by Burnham and Anderson (2002).
Inter-annual (year to year) variation in rainfalls: Sequences of
seasons favourable to growth of both grassy and woody fuels
can be expected to influence subsequent fire patterns (Meyer
2004), with fire being more frequent and affecting larger areas
after favourable seasons. Effects are expected to vary among
climatic zones with periods of high rainfall, which leads to
greater subsequent fire risk in mesic environments dominated
by woody vegetation, and in arid environments where grassy
fuels may accumulate over many years.
NDVI : This index more directly captures variation in seasonal
shifts in vegetation condition than measures of rainfall. It is
hypothesised that higher values will be followed by greater
fire activity. It should be noted that rainfall variation and
NDVI were treated in separate, competing models and never
entered into the same model.
Prior fire: The broad hypothesis examined is that prior fire may
inhibit fire in subsequent years and reduce areas burned by
consuming fuels and so reducing probability of ignitions as
well as size of burns. This effect was thought to be most
likely in mesic zones where a greater proportion of fuel is
woody and so replaced more slowly than in areas dominated
by grasses and in arid areas where both grassy and woody
fuels may accumulate in the landscape over long periods.
In the array of candidate models capturing this set of hypotheses, only models including main effects are presented. All
proportions were arcsine transformed before analysis.
Results
Spatial variation in fire incidence and extent
Fire activity (FHS) over the assessment period was markedly seasonal (Fig. 1). FHS were scattered across much of the continent
in the first quarter, concentrated in the south-western corner and
in northern Australia in the second, increased in activity across
the north, east coast and associated hinterland during the third,
and were concentrated across the north, east, western centre and
in the south-west corner, during the fourth. However, for much of
Australia, the areas burned are limited compared with frequent
1000
(9)
(8)
800
(7)
600
(6)
400
(5)
(4)
Rainfall (mm)
(2) Temporal variation in fire incidence and extent
In addition to the ‘landscape model’ that best explains variation
in average fire extent and fire frequency in tiles over the whole
study period, sources of annual variation in fire extent and incidence were also examined. This was done by adding rainfall and
NDVI variables aggregated over calendar years to the best landscape model. Influence of prior fire was also examined. Annual
figures for rainfall, NDVI and prior fire were introduced into statistical models containing RAINCLASS individually. A range of
time lags between fire observations as response variable and rainfall, NDVI, and prior fire were considered in annual increments
from observations taken in the same year (for rainfall and NDVI)
and from 1 to 4 years earlier (for rainfall, NDVI and prior fire).
In addition, aggregates of figures over two year periods were
also explored (after Meyer 2004).
The hypotheses modelled were:
(10)
200
(3)
(2)
(1)
0
1
2
3
4
Quarter
Fig. 5.
region.
Mean quarter rainfall distribution (1969–2004) per RAINCLASS
extensive burning evident in northern and parts of central Australia (Fig. 2). The magnitude of recurrent burning in northern
Australia is readily appreciated by comparison with the southeast Australian bushfires of 2002–2003 (circled in Fig. 2) which
affected ∼20 000 km2 .
To explore the geographical and longer-term temporal patterning of fire in detail, we derived an unsupervised classification
of 36 years (1969–2004) of quarter-annual rainfall records. The
classification process generated ten geographic RAINCLASS
regions (Fig. 3), which vary markedly in seasonal rainfall distribution (Fig. 5) and fire patterning (Table 2). The more-or-less
aseasonal southern and central Australian low rainfall regions
exhibit low mean FAA (≤5% p.a.), whereas monsoonal (summer) high rainfall, northern regions exhibit high mean annual
FAA. An apparent exception concerns the relatively small, high
rainfall, north-east Australian wet tropics (mean FAA = 5%) –
a region that encompasses marked internal rainfall variability.
Excluding the wet tropics, the five other northern Australian
regions contributed 71% of national mean FAA, with arid Australia contributing a further 26%. There is a linear relationship
between FAA per RAINCLASS region with rainfall seasonality
(Fig. 6).
Rainfall conditions over the study period were representative
generally of longer term trends, with notable exceptions being:
(1) throughout the 2002–2003 fire season southern Australia was
in the grip of drought and subject to extreme fire-weather conditions; and (2) rainfall over much of central Australia during the
latter part of the study period was well above average, thereby
stimulating grassy fuels and, subsequently, increasing fire proneness. Similar rainfall conditions in central Australia during the
mid-1970s also resulted in significantly increased fire activity
(Griffin et al. 1983; Allan and Southgate 2002).
The mean monthly proportions of FHS and FAA were mostly
similar within rainfall regions (Fig. 7); the exceptions being
Regions 5 and 3 in January and February, respectively, which
possibly reflects the influence of pyrocumulus cloud on FHS
detection associated with the 2002–2003 south-east Australian
Bushfires ‘down under’
Int. J. Wildland Fire
369
Table 2. Mean Fire Hot Spots (FHS) (1999–2005), Fire Affected Area (FAA) (1997–2004) and lightning (2004–2005) activity per RAINCLASS
region
RAINCLASS
1
Southern arid
2
Central arid
3
Southern mesic
4
Northern semi-humid
5
East coast semi-humid
6
Northern sub-coastal humid
7
Northern coastal humid
8
Top End and Cape York humid
9
Wet Tropics mesic
10 Northern Cape York humid
Australia
Area (km2 )
3 223 164
1 892 732
727 559
919 917
163 006
411 728
147 054
99 911
57 563
40 702
7 683 336
Mean lightning
strikes (no.
km−2 year−1 )
Mean FHS
(no. km−2
year−1 )
Mean FAA
(km2 year−1 )
Quarter 1
Seasonal distribution of FAA per RAINCLASS (%)
Quarter 2
Quarter 3
Quarter 4
Total
0.06
0.14
0.08
0.34
0.18
0.61
1.10
1.04
0.47
0.76
0.19
0.05
0.15
0.15
0.24
0.26
0.31
0.56
0.48
0.40
0.29
0.14
44 576
94 618
4307
167 177
1079
117 668
55 642
36 199
2610
9098
532 974
0.26
0.39
0.41
0.33
0.22
0.17
0.03
0.02
0.05
0.03
0.30
0.06
0.56
0.03
3.02
0.01
7.19
8.20
11.25
0.22
0.11
1.15
0.29
1.65
0.05
6.15
0.16
12.12
20.90
17.78
1.27
11.80
2.48
0.77
2.40
0.11
8.68
0.28
9.10
8.71
7.19
2.99
10.42
2.68
1.38
5.00
0.59
18.17
0.66
28.58
37.84
36.23
4.54
22.35
6.61
Table 3. Relative (%) distribution of fuel types among rainfall classes
The highly modified class includes agricultural areas, urban sites and plantations. Each region also includes areas dominated by water or for which data
were unavailable (h = hummock, t = tussock, a = Acacia, c = Chenopod; ma = Mallee, open = open woodland, lc = low closed woodland, r = rainforest,
eo = Eucalypt open forest, eto = Eucalypt tall open forest, elo = Eucalypt low open forest)
Fuel type (000 km2 )
RAINCLASS
1
2
3
4
5
6
7
8
9
10
Southern arid
Central arid
Southern mesic
Northern semi-humid
East coastal semi-humid
Northern sub-coastal humid
Northern coastal humid
Darwin-Cape York humid
Wet Tropics mesic
Northern Cape York humid
Grassland
Shrubland
h
t
a
c
ma
open
lc
r
eo
eto
elo
21.9
38.6
0.0
34.5
0.0
1.0
0.1
0.0
0.0
0.0
7.0
18.2
21.7
15.8
11.3
3.8
2.2
9.8
7.3
6.6
13.2
9.0
0.1
6.2
0.0
0.3
0.0
0.2
0.0
0.0
14.7
3.4
0.3
0.4
0.4
0.9
1.8
1.2
0.7
0.2
6.5
2.1
1.4
0.0
0.1
0.0
0.0
0.0
0.0
0.0
18.0
17.7
9.5
26.3
15.8
47.4
64.2
39.2
13.7
22.6
8.2
7.5
15.7
15.9
12.9
43.2
5.4
27.3
37.5
61.9
0.0
0.0
1.2
0.1
2.9
0.0
0.1
2.0
18.6
5.4
0.4
0.3
13.5
0.2
32.8
1.6
24.8
18.4
10.9
2.7
0.0
0.1
3.9
0.0
0.3
0.0
0.0
0.0
0.0
0.0
0.3
0.1
0.3
0.0
0.0
0.0
0.0
0.0
0.2
0.0
bushfires. Most landscape fire appears to be anthropogenic in origin, given weak temporal associations between fire activity and
lightning seasonality (Fig. 7). This is particularly evident in fireand relatively lightning-prone northern Australia where lightning is absent over most of the burning (mid-year dry) season.
The powerful influence of climatic regimes was confirmed
by statistical modelling of the annual average proportion of 0.5◦
tiles burned with the explanatory variables vegetation type, fuel
type, elevation, surface roughness, land use and cadastral density (Table 1). The best landscape model included the variables
‘RAINCLASS’, ‘dominant fuel type’, ‘proportion of tile over
which the fuel type was dominant’, and the ‘average size of land
parcels (individual properties that exceed 40 ha)’ (Table 4), and
explained ∼70.1% of null deviance, of which the great majority
(60.0%) was accounted for by membership of RAINCLASS
(Table 5). After taking account of RAINCLASS, tiles with larger
covers of the hummock (Spinifex: Triodia spp.) grassland fuel
type were most affected by fire, and those dominated by rainforest least burned. Tiles with larger average property sizes also
exhibited burning over larger areas. The best statistical models
Woodland
Forest
Highly modified
8.1
2.2
29.7
0.0
19.4
0.1
0.1
0.6
8.2
0.0
were broadly similar when frequency of occurrence of fire (number of times fire was recorded anywhere in the tile during eight
years), or the probability of fire being recorded in a tile at all
over the eight year study period, were treated as responses.
At the extensive spatial scales of our analyses, fire incidence
was not closely associated with land use (Table 4). This is perhaps unsurprising, given that incentives to use or exclude fire
can vary markedly within classes. In some pastoral regions,
for example, fire is assiduously suppressed, while in others it
is actively employed as a management tool to inhibit growth of
woody plants. A few forms of spatially extensive agriculture (e.g.
sugarcane in easternAustralia; burning of wheat stubble in southwestern Australia) and forestry, use fire as part of management.
However, at more local scales (not addressed in these analyses)
it is apparent that land uses and property sizes may exert powerful influences on fire patterns. A salient example from fireprone
northern Australia concerns the apparent influence of the intensity of pastoral property infrastructural development (fencing,
tracks) on regional-scale fire frequency (CP Yates et al., unpubl.
data).
370
Int. J. Wildland Fire
J. Russell-Smith et al.
Fire affected area
Fire hotspots
R2 ⫽ 0.979
30
20
10
0
0
20
40
60
80
100
Ratio of mean rainfall in highest:lowest quarter
Fig. 6. Mean fire extent (FAA) v. ratio of mean rainfall in largest/smallest
quarters, for each of ten RAINCLASS regions.
Temporal variation in fire incidence and extent
While understanding year-to-year variation in fire extent is not
a principal focus of this contribution given the limited temporal scale of the FAA dataset, we found that patterns of annual
FAA varied among regions over the eight years of the study
(repeated-measures GLM, P < 0.0001). Statistical models
relating year-to-year variation in areas burned to variation in
rainfall, NDVIsd, or prior fire (both separately and in combination) varied among regions, most often included rainfall in
the preceding year, fire in the preceding year, or fire and rainfall totalled over the preceding two years (Table 6). In no case
did rainfall, or NDVIsd observed in the same year as the fire
was recorded, appear in the best models. The proportion of
deviance explained varied markedly, with models that contained
these variables performing best in northern regions and worst in
southern Australia.
More fire was observed in years with above average rainfall in
the preceding one or two years and, interestingly, areas burned
were also greater in cells that had greater areas burned in the
preceding one or two years. Perhaps the most important insight
from these simple models is that fire in prior years was positively
associated with contemporary fire, rather than the negative association hypothesised on the grounds that prior fire reduces fuel
loads and consequently reduces fire risk and size.
Discussion
This quantitative assessment of contemporary Australian fire
patterning is undertaken with reference to broad seasonal rainfall classes and it is important to appreciate the issues of scale
involved. First, these rainfall classes do not describe homogenous ecosystems (i.e. vegetation/fuel structure) nor land use
types; for example, the relatively densely populated 727 000 km2
Proportion of FAA, FHS, lightning
Mean proportion of RAINCLASS region burnt (%)
40
40
30
20
10
0
40
30
20
10
0
40
30
20
10
0
40
30
20
10
0
40
30
20
10
0
40
30
20
10
0
40
30
20
10
0
40
30
20
10
0
40
30
20
10
0
40
30
20
10
0
Lightning
1. Southern arid
2. Central arid
3. Southern mesic
4. Northern semi-humid
5. East coast semi-humid
6. Northern sub-coastal humid
7. Northern coastal humid
8. Top End and Cape York humid
9. Wet Tropics mesic
10. Northern Cape York humid
Jan
Feb Mar
Apr May Jun
Jul
Aug Sep
Oct
Nov Dec
Month
Fig. 7. Mean monthly distribution of FHS (1999–2005), FAA (1997–
2004) and lightning incidence (2004–2005) per RAINCLASS region.
Southern mesic RAINCLASS comprises mostly highly modified (agricultural) lands (30%), then woodland fuel types (27%),
grassland fuels (22%), and forest fuels (19%) (Table 3). As
such, it is not our purpose here, and neither are our coarse
spatial resolution analyses appropriate, to describe within-class
correlations of patterning of large fires in habitat-scale detail.
Similarly, the observation period comprises just eight years
of FAA records and, while this may be adequate to describe patterns of high frequency fire recurrence in fire-prone savannas,
it is patently inadequate to describe typically multi-decadal fire
return intervals in, for example, temperate forest and semi-arid
vegetation types. As noted previously, however, the observation
period covered both sequential years of below and well above
Bushfires ‘down under’
Int. J. Wildland Fire
371
Table 4. Comparison of models relating the average proportion of the area of 0.5 degree cells burned annually,
based on the annual average across the period 1997 to 2004, to landscape variables (refer to Table 1 for explanation
of descriptors)
AICc is the value of the Akaike Information Criterion corrected for the number of parameters. k is the number of parameters
in the model. AICc is the difference between the AICc for the candidate model and the best model in the set. The lower the
value of AICc, the closer the model to the best model. As a rule of thumb, models with AICc exceeding 10 are treated
as having effectively no support compared with the best model. Models differing in AICc by 2 or less are often treated as
indistinguishable (Burnham and Anderson 2002). Weight is the Akaike weight which provides a measure of support for
the candidate model relative to other models in the set. Null deviance for most models (depending on missing values) was
128.9 or slightly lower. The best model explained ∼70.1% of null deviance. Models based on land use performed poorly
compared to those including vegetation or fuel types
Number
Description
k
AICc
AICc
Weight
Residual deviance
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
RAINCLASS (r)
r + domveg
r + domfuel
r + domluf
r + domlub
r + numlus
r + avparea40
r + numparc40
r + elevrange
r + domlub + %domlu
r + domlub + numlus
r + domveg + %domveg
r + domveg + avparea40
r + domveg + numparc40
r + domveg + elevrange
r + domveg + numlus
r + domveg + %domveg + avparea40
r + domveg + %domveg + numparc40
r + domveg + %domveg + elevrange
r + domfuel + %domfuel
r + domfuel + avparea40
r + domfuel + numparc40
r + domfuel + elevrange
r + domfuel + %domfuel + avparea40
r + domfuel + %domfuel + numparc40
r + domfuel + %domfuel + elevrange
11
34
25
33
16
12
12
12
12
17
12
35
35
35
35
35
36
36
36
26
26
26
26
27
27
27
−3713
−4358
−4477
−4111
−4031
−3780
−4128
−3783
−3750
−4034
−3780
−4378
−4549
−4391
−4381
−4375
−4555
−4414
−4407
−4515
−4579
−4480
−4508
−4597
−4525
−4558
885
240
121
486
566
816
469
815
847
563
552
219
48
206
216
223
42
183
191
82
18
117
89
0
72
39
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
∼0
0.9998
∼0
∼0
51.5
40.9
39.6
44.5
46.2
50.3
43.8
49.2
50.8
46.1
50.3
40.6
37.4
39.5
40.6
40.7
37.3
39.1
40.2
39.0
37.3
38.6
49.6
37.0
37.9
38.5
average rainfall in arid central Australia. Assuming that fire frequencies observed here on the RAINCLASS spatial scale are
representative generally of longer term trends, then our temporal sample illustrates that a mean of 90% of FAA occurs in
central and northern Australian RAINCLASSES (2, 4, 6–10);
an annual mean of <1% of the politically significant Southern
mesic RAINCLASS is fire affected (Table 2).
Existing continental-scale models that purportedly describe
fire seasonality (Luke and McArthur 1978; Walker 1981),
and frequency of hazardous or large fires (Cheney 1979;
Commonwealth of Australia 1996), do not adequately represent
contemporary fire patterning (Fig. 8a, b). A refined model is
presented (Fig. 8c) that characterises fire seasonality and extent
derived from our satellite-based observations, in six broad zones
based on our rainfall regions. In the absence of marked changes
in cadastres, land use and population distribution changes, this
model is likely to reflect general patterning in Australian fire
seasonality and distribution for decades to come, but with the
caveat that periods of very high to extreme fire danger are
likely to increase throughout Australia under predicted climate
change scenarios, especially substantially higher temperatures
and increased length of fire season (Williams et al. 2001; Ellis
et al. 2004).
The critical features of this model are that incidence of fire in
Australia is most powerfully influenced by the linked attributes
of vegetation/fuel structure and seasonal variation in rainfall.
Fire frequencies and fire extent increase with seasonality of rainfall and hence rise markedly from lows in southern Australia
to highs in the intensely seasonal tropics of northern Australia
(Fig. 2). Landscapes dominated by grasses (especially hummock
grasses), including open woodland and open forest, are more
likely to burn. In rural areas, the lower incidence of fire in southern Australia is reinforced (Tables 4, 5) by the greater control
that can be exercised over fire in more densely settled areas
with smaller properties and greater access to infrastructure and
institutions to facilitate fire management. Clearly, however, as
illustrated by the events of 2002–2003 (Fig. 2), large intense fires
occasionally overwhelm even the best prepared and equipped
regions. The impacts of these events on human life, property
and environmental values (including biodiversity) have been
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Int. J. Wildland Fire
J. Russell-Smith et al.
Table 5. Parameter estimates and related statistics for the best model from the array of candidate
models considered and shown in Table 4, relating the percentage of each 0.5◦ × 0.5◦ cell burned to
landscape variables (n = 3025)
The ‘Southern arid’ RAINCLASS region is aliased and coefficients indicate variation relative to that class
(coefficient for Southern arid taken to be zero). Note that coefficients relate to arcsine transformed values
of the percentage of cell area burned and percentage of cell area covered by the dominant vegetation type.
Null deviance was 127.2 and residual deviance 37.0
Variable
Estimate
Std error
Intercept
RAINCLASS
Central arid
Southern mesic
Northern semi-humid
East coast semi-humid
Northern sub-coastal humid
Northern coastal humid
Top End and Cape York humid
Wet Tropics mesic
Northern Cape York humid
Dominant fuel type
Acacia
Agricultural
Chenopod
Eucalypt low open forest
Eucalypt open forest
Eucalypt tall open forest
Hummock grassland
Low closed woodland
Mallee
Open woodland
Rain forest
Tussock grassland
Urban
Water
Percent cover of dominant fuel type
Average area of parcels >40 ha
−0.1058
0.0905
0.0462
0.3197
0.0456
0.4594
0.4857
0.5654
0.147
0.3241
0.1128
0.0744
0.0477
0.1334
0.0766
0.055
0.1901
0.0951
0.0919
0.1375
−0.018
0.0429
0.0549
0.0361
0.0404
0.00002
t value
Pr (>|t|)
0.0334
3.167
0.0016
0.0057
0.0081
0.0073
0.0147
0.0141
0.0147
0.0182
0.0224
0.0252
15.947
5.676
43.756
3.11
44.141
33.138
31.005
6.549
12.839
<0.0001
<0.0001
<0.0001
0.0019
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.0333
0.0332
0.0336
0.0722
0.0343
0.0444
0.0331
0.0392
0.0344
0.0328
0.0489
0.033
0.0655
0.0442
0.0091
0.000002
3.389
2.24
1.418
1.847
2.234
1.237
5.736
2.887
2.668
4.198
−0.368
1.302
0.838
0.817
4.443
8.514
0.0007
0.0252
0.1563
0.0649
0.0256
0.2162
<0.0001
0.0039
0.0077
<0.0001
0.7127
0.1932
0.4019
0.4141
<0.0001
<0.0001
Table 6. Summary of models relating the extent of fire in 0.5 degree cells during each fire season (see text) to deviation from the long-term mean
rainfall, departures of NDVIsd during the rain season (see text) from the series minimum (1992–2004), and prior fire, for each of the regions defined
by the RAINCLASS classification
Where the weight for the best model is at least twice the value of the next highest weight, only one model is shown. Note that sample size varies with the
number of tiles making up different regions, and also with the lagging of observations of prior fire. For each year of lagging, 1 year of observations is lost
RAINCLASS
Model parameters
1
2
3
4
5
6
7
Annual rain in preceding year + total fire in preceding two years
Annual rain in preceding year + fire in preceding year
Annual NDVIsd in preceding year + annual fire in preceding year
Annual rain in preceding year + fire two years earlier
Annual NDVIsd in preceding year + annual fire in preceding year
Total rain in preceding two years + total fire in preceding two years
Total fire in preceding to years
Total rain in preceding 2years + total fire in preceding two years
Total NDVIsd in preceding two years + total fire in preceding two years
Total fire in preceding two years
Total rain in preceding two years + total fire in preceding two years
Annual fire two years earlier − annual NDVIsd in preceding year
Annual fire two years earlier
Total rainfall in preceding two years + total fire in preceding two years
Southern arid
Central arid
Southern mesic
Northern semi-humid
East coast semi-humid
Northern sub-coastal humid
Northern coastal humid
8 Top End and Cape York humid
9 Wet Tropics mesic
10 Northern Cape York humid
All regions
N
k
Relative weight
% Dev
10 096
5408
2768
2568
672
1264
616
616
616
376
248
184
184
24 200
4
4
4
4
4
4
3
4
4
3
4
4
3
4
1.000
1.000
0.992
1.000
0.601
0.692
0.367
0.383
0.208
0.564
0.810
0.272
0.212
1.000
11.1
18.1
1.9
21.0
9.9
22.4
28.7
29.0
28.8
30.5
23.7
51.4
50.8
41.6
Bushfires ‘down under’
Int. J. Wildland Fire
(a)
DARWIN
TENNANT CREEK
MT ISA
PORT HEDLAND
ALICE SPRINGS
BRISBANE
GERALDTON
KALGOORLIE
SYDNEY
PERTH
ADELAIDE
Winter and spring
Summer
Spring
Summer and autumn
CANBERRA
MELBOURNE
HOBART
Spring and summer
(b)
1
2
3
4
5
Occurrence of large bushfires
Season
Once in more than 20 years
1 Winter and spring
Once every 20 years
2 Spring
Once every 10 years
3 Spring and summer
Once every 5 years
4 Summer
Once every 3 years
5 Summer and autumn
Fig. 8. Depictions of Australian fire activity. (a) widely cited map, following Luke and McArthur (1978).
(b) recent official map purporting to show ‘occurrence of large bushfires’ (Commonwealth of Australia
1996), derived from Cheney (1979). (c) map of contemporary fire seasonality and extent as derived here
which (i) lumps together certain RAINCLASS regions based on similar fire activity seasonality based on
FHS and FAA (refer Fig. 7), (ii) indicates main burning period (given as black horizontal bar in legend,
derived from Fig. 7) and the mean extent (%) of FAA per region, 1997–2004 (refer Table 2).
373
374
Int. J. Wildland Fire
J. Russell-Smith et al.
(c)
Generalised extent and seasonal distribution of fire occurrence in Australia 1997–2004
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Mean annual
FAA %
1
3
5
19
27
35
Fig. 8.
extensively documented (Ellis et al. 2004), and contribute to
a substantial national policy focus on southern Australian contemporary fire patterns and their consequences. This emphasis
contributes to the neglect of globally significant fire issues in
less populated central and northern regions.
Our observations illustrate the lack of extensive burning evident generally in populous south-eastern Australia. Ever since
the formative ‘Black Friday’ bushfires of January 1939, multiple boards of inquiry have urged concerted prescribed fuel
reduction burning in forested southern Australia as a mitigating
practice (Pyne 1991, 2006). Many studies from temperate Australia (McCarthy and Tolhurst 2001; McCaw et al. 2003) and
elsewhere (Fernandes and Botelho 2003) illustrate the effectiveness, within limitations, of prescribed burning in reducing fuel
loads, ameliorating subsequent wildfire behaviour, and assisting management operations. Today, however, such practice is
(Continued)
increasingly restricted in extent given political, operational and
ecologic complexities and constraints (Bradstock et al. 1998;
Cary et al. 2003; Esplin et al. 2003; Ellis et al. 2004; Pyne 2006).
While prescribed burning is not a panacea under extreme fire
weather conditions, the alternative of complete fire exclusion is
practically untenable given the coincidence of lightning and peak
fire weather conditions in forested southern Australia; for example, most of the 2002–2003 south-eastern bushfires were ignited
by lightning that emanated from dry thunderstorms (Esplin et al.
2003; Ellis et al. 2004). For south-eastern Australia at least,
continued periodic bushfire conflagrations under drought conditions, particularly associated with El Nino Southern Oscillation
(ENSO) events, may be anticipated to be the norm (Hennessy
et al. 2006; Pyne 2006).
For rainfall-event driven fire-prone central Australia, and
annually fire-prone northern Australia, accumulating evidence
Bushfires ‘down under’
points to contemporary fire patterns having extensive and substantial impacts on biodiversity and soil erosion (Dyer et al.
2002; Ellis et al. 2004), greenhouse gas emissions (Kondo
et al. 2003; AGO 2006), and, more locally, on respiratory health
(Johnston et al. 2002). Major impacts on biodiversity components attributable to frequent extensive fires, especially firesensitive plant species and assemblages, are widely reported both
from central and northern Australia (e.g. Bowman and Panton
1993; Allan and Southgate 2002; Russell-Smith et al. 2003b).
Direct impacts of current fire regimes on faunal components
are less clear, although it is well recognised that fine-grained
spatio-temporal fire mosaics are a major contributor to habitat
heterogeneity across the vast, mostly topographically subdued
landscapes of the Australian rangelands (Woinarski et al. 2005;
Burrows et al. 2006).
Such impacts on biodiversity in central and northernAustralia
at least, reflect substantial changes to landscape burning patterns
established under prior Aboriginal occupancy. Accumulating
evidence points to: (a) widespread replacement of relatively finescale fire mosaics with more homogeneous patterns – including
both frequent and extensive wildfires over the greater part, and
fire exclusion in more densely settled agricultural regions of
western Queensland; and (b), significant shifts in fire seasonality (e.g. Bowman 1998; Dyer et al. 2002; Russell-Smith et al.
2003b; Burrows et al. 2006). Across northern Australia, for
example, extensive wildfires occur predominantly in the latter part of the (6–8 month) dry season period under relatively
severe fire-weather conditions, in contrast to numerous ethnographic and historical accounts which emphasise that, under
Aboriginal occupancy, burning was conducted throughout the
dry season (Russell-Smith et al. 2003b). While available historical and ethnographic records for southern Australia generally are
limited, it is equally apparent that marked regional changes in the
extent and seasonality of burning have also occurred since European colonisation (e.g. Hallam 1975; Bowman 1998; Abbott
2003).
At much larger spatial scales in northern Australia, contemporary Australian savanna burning patterns have significant
implications for accountable national greenhouse gas (CH4 ,
N2 O) emission estimates which, in 2004, comprised ∼2% of
total emissions under Kyoto accounting provisions (AGO 2006).
However, such provisions do not account directly for savanna
burning emissions of CO2 , since it is assumed that CO2 emissions in one burning season are negated by vegetation growth in
subsequent growing seasons (IPCC 1996). This assumption only
holds true in practice when the ecosystem and its carbon stocks
remain stable in the long-term. If the fire regime leads to a change
in vegetation structure then the assumption may be violated.
Cook et al. (2005), for example, found a substantial decline in
above-ground carbon stocks in response to more severe savanna
fire regimes in northern Australia. The issue has been the subject
of extensive discussion in the IPCC during the development of
the Good Practice Guidance (IPCC 2003). However, it remains
a basic assumption underpinning the default (Tier 1) methodologies. In managed ecosystems, the higher tier methodologies
used by some nations for greenhouse gas accounting for biogenic sources may be able to account for changes in carbon
storage induced by fire.
Int. J. Wildland Fire
375
Although excluded from national accounts of direct greenhouse gases, CO2 , together with more reactive species (the
ozone precursors comprising CO, volatile organic compounds
and oxides of nitrogen: Shirai et al. 2002) that are released
into the atmosphere over a typically long burning season are
likely to have a substantial impact on regional atmospheric
composition, and its interannual variability. Applying current
Australian National Greenhouse Gas Inventory methodology
(AGO 2006), we can estimate that such CO2 emissions amount
to a mean annual 218 Mt CO2-e over the period 1997–2004 for
the 1.9 M km2 tropical savannas region (equivalent to 38.5% of
Australian net greenhouse emissions for 2004), derived from an
estimated mean annual FAA (savannas region) of 334 502 km2
that consumes estimated mean fuels of 148 Mt (dry matter) y−1 .
A major challenge is to reduce the annual extent of north
Australian wildfire. While prescribed burning (including the
application of labour-intensive indigenous management models
(Yibarbuk et al. 2001) can be a solution at local and, using aerial
ignition sources, at broader regional scales (Dyer et al. 2002),
costs associated with such programs today are prohibitive given
low population densities, limited infrastructure and national
support, and a vast, mostly unmodified savanna landscape. In
effect, where annual fire incidence and extent are greatest, the
resources needed for effective management are least available
(Whitehead et al. 2002). Official focus on bushfires as being
a characteristically southern Australian phenomenon and management issue clearly requires reappraisal, if genuinely national
strategies for fire management are to be developed and these
important issues are to be dealt with effectively.
Acknowledgements
FAA mapping over the eight year study was undertaken by Nat RaisbeckBrown, Jacqui Marsden, Belinda Heath. Lightning data were assembled by
Stefan Maier. This project has been supported by the Tropical Savannas Management Cooperative Research Centre, the Northern Territory Department
of Natural Resources, Environment & the Arts, the Western Australia Department of Land Information, and Australian Government programs including
SOE, NHT, RIRDC.
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http://www.publish.csiro.au/journals/ijwf